论文标题
汽车：临床预测模型部署的自动医疗代码映射
AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment
论文作者
论文摘要
给定对来自源站点数据培训的深度学习模型，如何自动将模型部署到目标医院？如何容纳不同医院的异质医疗编码系统？标准方法依赖于现有的医疗代码映射工具，这些工具具有重大的实际限制。 为了解决这个问题，我们建议以粗到5的方式自动绘制不同EHR系统的医疗代码：（1）本体论级别的对齐：我们利用本体结构来学习源和目标医疗编码系统之间的粗略对齐； （2）代码级的改进：我们使用教师学生框架以细粒度的代码级别来完善对齐方式。 我们使用两个真实的EHR数据集使用多种深度学习模型来评估自动图：EICU和MIMIC-III。结果表明，用于死亡率预测的Automap可实现高达3.9％（AUC-ROC）和8.7％（AUC-PR）的相对改善，用于停止估计的2.7％（AUC-ROC）和3.7％（AUC-ROC）和3.7％（F1）。此外，我们表明自动制品可以在编码系统上提供准确的映射。最后，我们证明自动制品可以适应两个具有挑战性的方案：（1）在完全不同的编码系统和（2）之间映射完全不同的医院之间。
Given a deep learning model trained on data from a source site, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medical code mapping tools, which have significant practical limitations. To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework. We evaluate AutoMap using several deep learning models with two real-world EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction, and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further, we show that AutoMap can provide accurate mapping across coding systems. Lastly, we demonstrate that AutoMap can adapt to the two challenging scenarios: (1) mapping between completely different coding systems and (2) between completely different hospitals.
